Big Data: Five Mega Trends Shaping the Pharmaceutical Industry

By Patricia Van Arnum - DCAT Editorial Director

June 17, 2014

A recent analysis by Ernst & Young concludes that the era of big data has arrived, but are pharmaceutical companies ready?

So what is the next gamechanger for the pharmaceutical value chain? Answer: big data. A recent analysis by Ernst & Young concludes that greater access to a wide variety of big data and more powerful analytics, combined with advances in cloud-computing and predictive analytics platforms, are enabling pharmaceutical companies to achieve better knowledge across the entire value chain. The analysis looks at five mega trends involving big data that are shaping the pharmaceutical industry's strategic and operational focus.

"Enterprise transformation led by big data-driven analytics is no longer a ‘pie-in-the-sky' ambition for life sciences companies, but rather an essential and achievable component for their sustained success."

--Todd Skrinar, a principal in the Ernst & Young LLP Advisory Life Sciences practice

"Enterprise transformation led by big data-driven analytics is no longer a ‘pie-in-the-sky' ambition for life sciences companies, but rather an essential and achievable component for their sustained success," said Todd Skrinar, a principal in the Ernst & Young LLP Advisory Life Sciences practice, in a company release. "Organizations that understand how to effectively leverage internal and external data relevant to their products, markets, and customers will create a distinct advantage for themselves in today's patient-centric and outcomes-focused healthcare system. Those that do not invest may put their ability to effectively compete in the future in peril."

The Ernst & Young report identifies five "mega trends" that have created a collaborative environment that gives life-sciences and healthcare organizations the opportunity to use big data and advanced analytics to create patient centricity in their operations and deliver measurable improvements in outcomes (1). These mega trends include:

  1. Disruptive consumer technology that enables real-time monitoring of users' health status, including blood pressure, pulse rates, and glucose levels.
  2. The advancement of personalized-medicine approaches targeting specific patient genomic segments.
  3. The proliferation of advanced analytics that provide individuals, at all levels of an organization, with the ability to integrate large volume sets of big data from a variety of sources in real-time.
  4. Maturing capabilities of cloud computing, which are overcoming some of the continued concerns life-sciences companies have as it relates to data security and privacy.
  5. The need to reduce healthcare costs in health care systems globally, which includes increased demands by public and private payers that life sciences companies demonstrate the real-world impact of their products on outcomes.

 

The report also provides insights into how life-sciences organizations can prepare for and capitalize on the increasing impact of data and analytics on the healthcare ecosystem, including how to:

  • Manage data and analytics projects as a portfolio of assets, whereby the value derived from short-term projects is balanced with longer-term, more complex opportunities.
  • Assess the strategic capabilities and resource requirements needed to compete in data and analytics.
  • Implement new advanced analytics capabilities in a way that increases the likelihood of their success.

 

"There are vast opportunities for life sciences organizations to utilize big data-driven analytics in new ways to help enable patient centricity, drive innovation, streamline operations, and collaborate to demonstrate tangible and measurable value."

--Ric Cavieres, a principal in the Ernst & Young LLP Advisory Life Sciences practice

"There are vast opportunities for life science organization to utilize big data-driven analytics in new ways to enable patient centricity, drive innovation, streamline operations, and collaborate to demonstrate tangible and measurable value," said Ric Cavieres, a principal in the Ernst & Young LLP Advisory Life Sciences practice, in the company press release. The Ernst & Young report notes that the first step in harnessing the power of big data and advanced analytics capabilities is to manage the data and analytics projects as a portfolio of assets, creating what it calls "an agile analytics approach." The report says by managing analytics projects as a portfolio, much like individual investors manage their financial portfolios, life sciences and healthcare organizations can use an agile analytics approach to balance value (long-term, moderate value, low-risk), growth (medium-term, higher value, medium risk), and aggressive growth (short-term, high-value, high-risk) assets. A typical portfolio may include analytics in clinical development, manufacturing and supply chain, and sales and marketing. In manufacturing and supply chain, key analytics include data for supplier compliance, inventory management, demand planning and forecasting, and manufacturing asset productivity and effectiveness (1).

The report outlines four elements of an agile analytical network. The report notes that a typical analytics life cycle requires four initiatives: innovation, incubation, industrialization, and substainability. Under innovation, the key emphasis is on identifying key business problems and driving innovation to develop a solution that produces a proof of concept or prototype. Under incubation, using the proof of concept, the user should scale the analytics initiative to evaluate value across larger target beneficiaries and test the model across additional cycles and determine whether there are enough benefits in the project to proceed to the next stage. If there are, the next step is industrialization. In this stage, the solution is moved from proof of concept to validation, deployment, and monitoring with the result being a production-scale solution that proves value and benefits. And the last initiative is sustainability, which is marked by maintaining the analytics solutions and providing solutions to enable continued value delivery (1).

The report recommends several steps to accelerate the innovation and incubation steps, which often is required due to the change occurring in organizations. First, is to improve competencies of available talent within the organization and positioning them to add value to be successful. Second is implementing a lean governance model that supports the collection, sharing, and reuse of analytics assets, where possible. Third is to define processes to maintain data and enhance data quality. Fourth is defining data-technology capabilities and establishing adaptable procedures to access technology assets, and lastly, it is developing and continuously maintaining a portfolio of analytics opportunities. The report also says that the organization should also create a value-realization framework by which to measure both qualitative and quantitative benefits (1).

"By using an agile analytics framework, organizations can significantly accelerate their analytics project delivery," concludes the report. "Agile analytics increases the engagement from the business and the ability to deliver data-driven insights in all areas where the organization uses analytics."

The Ernst & Young report outlines six steps organizations can use to position their strategic capabilities for success (1).

  1. Adopt data science as a cross-functional discipline. Organizations should offer data science or analytics support across the enterprise.
  2. Manage analytics as a portfolio. Organizations should deliver analytics  as a shared service that is governed using portfolio management discipline.
  3. Implement an enterprise analytics Center of Excellence (CoE). A CoE can increase analytics productivity; improve decision effectiveness; simplify data management, analytics performance and decision-making; and facilitate continuous analytics learning and innovation.
  4. Implement a shared analytics network. A common analytics network managed by the CoE enables the organization to share methods, tools, data and models in an environment where results of prior analyses can be factored into new analytics projects. It allows organizations spend less time seeking the right data and the right tools and more time performing analyses and driving decisions.
  5. Establish a structured change program. Transitioning an organization to a CoE-oriented operating model takes leadership, alignment, adoption and execution. A formal change management process helps successfully manage the shift and offers an opportunity to identify analytics talent within the company and retrain them for specific roles in the CoE.
  6. Partner for success. Whether determining an analytics strategy, implementing an analytics CoE, or sourcing data scientists, partnering with a third party can drive new analytics innovation and add capacity for existing analytics capabilities.

Reference
1. Ernst & Young, "Order from Chaos: Where Big Data and Analytics Are Heading, and How Life Sciences Can Prepare for the Transformational Wave" Report (June 2014).